To support and engineer the spatial coordination of distributed pervasive services, we propose a chemical-inspired model, which extends tuple spaces with the ability of evolving tuples mimicking chemical systems, i.e. in terms of reaction and diffusion rules that apply to tuples modulo semantic match. The suitability of this model is studied considering a self-adaptive display infrastructure providing nearby people with several visualisation services (advertisements, news, personal and social content). The key result of this paper is that general-purpose chemical reactions inspired by population dynamics can be used in pervasive applications to enact spatial computing patterns of competition and gradient-based interaction.

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Research fields like pervasive computing are showing that the interactions between components in large-scale, mobile, and open systems are highly affected by unpredictability: self-organising techniques are increasingly adopted within infrastructures aimed at managing such interactions in a robust and adaptive way. Accordingly, in this paper we discuss the framework of self-organising coordination: coordination media spread over the network are in charge of managing interactions with each other and with agents solely according to local criteria, making interesting and fruitful global properties of the resulting system appearing by emergenceprobability and timing typically playing a crucial role. We show that the TuCSoN coordination infrastructure can be used as a general platform for enacting self-organising coordination; we put it to test on two cases: an inter-space application of adaptive tuple clustering, and a intra-space application of chemical-like coordination reactions.

Recent coordination languages and models are moving towards the application of techniques coming from the research context of complex systems: adaptivity and self-organization are exploited in order to tackle the openness, dynamism and unpredictability of today's distributed systems. In this area, systems are to be described using stochastic models, and simulation is a valuable tool both for analysis and design. Accordingly, in this work we focused on modelling and simulating emergent properties of coordination techniques. We first develop a framework acting as a general-purpose engine for simulating stochastic transition systems, built as a library for the Maude term rewriting system. We then evaluate this tool to a coordination problem called collective sort, where autonomous agents move tuples across different tuple spaces according to local criteria, and resulting in the emergence of the complete clustering property.

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